MoSWIN: Monkey Search Optimization and SWIN-based Flood Classification Architecture

Vinay Dubey () and Rahul Katarya ()
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Vinay Dubey: Delhi Technological University
Rahul Katarya: Delhi Technological University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 12, No 13, 6275-6303

Abstract: Abstract Floods pose significant threats to both human life and infrastructure, necessitating timely and accurate monitoring for effective disaster management. Advancements in computer vision and machine learning have enabled the development of sophisticated systems for flood classification using imagery. In this study, we introduce MoSWIN, a novel framework that integrates Monkey Search Optimization (MSO) with the Swin Transformer to enhance the accuracy and robustness of flood classification models. The Swin Transformer, with its hierarchical architecture and shifted window mechanism, excels at capturing both local and global features in flood-related imagery. MSO is employed to extract high-level features from input images, facilitating improved feature engineering. Subsequently, the Swin Transformer performs classification tasks on the features extracted through MSO. Our proposed MoSWIN model outperforms several existing state-of-the-art approaches, achieving an accuracy of 96.53%. This indicates a significant improvement in image classification and optimization performance, leading to more effective flood detection. Experimental results demonstrate that MoSWIN can accurately distinguish between flooded and non-flooded areas, surpassing the performance of conventional methods.

Keywords: Convolutional neural network; Flood detection; Image segmentation; Monkey search optimization; Resnet-18; SWIN transformer (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04250-2

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